Assessing MODIS land surface temperature (LST) over Jeddah

• Autorzy: Assiri M.E.
• Języki publikacji: EN

Abstrakty

EN

Surface temperature is an important parameter for environmental as well as climate studies. Weather stations, however, provide limited information about heterogeneous spatial patterns of temperatures over wide areas, and satellite remote sensing is therefore a viable solution. The current study is designed to assess the land surface temperature (LST) as estimated by a moderate resolution imaging spectroradiometer (MODIS) onboard both the Terra and Aqua satellite systems from 2000 to 2014 over Jeddah. Station data from Jeddah airport was retrieved for the study period and statistical analysis was performed. The results of the study show that the correlation coefficient between MODIS day (night) time LST and station-based maximum (minimum) temperature is 0.40 (0.93) for Aqua and 0.52 (0.85) for Terra. Overall, the satellite-based minimum LST estimation is much better than maximum LST estimation. The results are further analyzed with respect to population trends and urban growth development in Jeddah. I concluded that satellite-based LST (MODIS Aqua and Terra) has great potential to represent ground station data over Jeddah, Saudi Arabia.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2017
• Tom: 26
• Numer: 4
• Opis fizyczny: p.1461-1470,fig.,ref.

Twórcy

autor

Assiri M.E.

• Department of Meteorology, Faculty of Meteorology, Environment, and Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi Arabia

Bibliografia

• 1. LI Z.-L., TANG B.-H., WU H., REN H., YAN G., WAN Z., SOBRINO J.A. Satellite derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14, 2013.
• 2. GILL S.E., HANDLEY J.F., ENNOS A.R., PAULEIT S., THEURAY N., LINDLEY S. Characterising the urban environment of UK cities and towns: A template for landscape planning. Landscape and Urban Planning, 87, 210, 2008.
• 3. KOLOKOTRONI M., GIRIDHARAN R. Urban heat island intensity in London: An investigation of the impact of physical characteristics on changes in outdoor air temperature during summer. Solar Energy, 82, 986, 2008.
• 4. ROSENZWEIG C., SOLECKI W. D., PARSHALL L., CHOPPING M., POPE G., GOLDBERG R. Characterizing the urban heat island in current and future climates in New Jersey. Environmental Hazards, 6, 51, 2005.
• 5. World Urbanization Prospectus The 2014 Revision Highlights. Available online: http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf (Accessed 02 March 2015).
• 6. GAGO E.J., ROLDAN J., PACHECO-TORRES R., ORDÓÑEZ J. The city and urban heat islands: A review of strategies to mitigate adverse effects. Renewable and Sustainable Energy Reviews, 25, 749, 2013.
• 7. VARDOULAKIS E., KARAMANIS D., FOTIADI A., MIHALAKAKOU G. The urban heat island effect in a small Mediterranean city of high summer temperatures and cooling energy demands. Solar Energy, 94,128, 2013.
• 8. MADLENER R., SUNAK Y. Impacts of urbanization on urban structures and energy demand: what can we learn for urban energy planning and urbanization management? Sustainable Cities and Society, 1, 45, 2011.
• 9. ROSENTHAL J.K., KINNEY P.L., METZGER K.B. Intra-urban vulnerability to heat-related mortality in New York City, 1997-2006. Health & Place, 30, 45, 2014.
• 10. SMARGIASSI A., GOLDBERG M.S., PLANTE C., FOURNIER M., BAUDOUIN Y., KOSATSKY T. Variation of daily warm season mortality as a function of micro-urban heat islands. J. Epidemiol. Community Health, 63 (8), 659, 2009.
• 11. RYU Y.H., BAIK J.J., KWAK K.H., KIM S., MOON N. Impacts of urban land-surface forcing on ozone air quality in the Seoul metropolitan area. Atmospheric Chemistry and Physics, 13, 2177, 2013.
• 12. CHUN B., GULDMANN J.M. Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landscape and Urban Planning, 125, 76, 2014.
• 13. WU H., YE L.P., SHI W.Z., CLARKE K.C. Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China. International Journal of Applied Earth Observation and Geoinformation, 32, 67, 2014.
• 14. VANCUTSEM C., CECCATO P., DINKU T., CONNOR S.J. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114, 449, 2010.
• 15. BENALI A., CARVALHO A.C., NUNES J.P., CARVALHAIS N., SANTOS A. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124, 108, 2012.
• 16. LIN S., MOORE N.J., MESSINA J.P., DEVISSER M.H., WU J. Evaluation of estimating daily maximum and minimum air temperature with MODIS data in east Africa. International Journal of Applied Earth Observation and Geoinformation, 18, 128, 2012.
• 17. ZHU W., LU A., JIA S. Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sensing of Environment, 130, 62, 2013.
• 18. OYLER J., DOBROWSKI S., HOLDEN Z., RUNNING S. Remotely Sensed Land Skin Temperature as a Spatial Predictor of Air Temperature across the Conterminous United States. Journal of Applied Meteorology and Climatology, 55, 1441, 2016.
• 19. WAN Z.M., DOZIER J. A generalized split-window algorithm for retrieving land- surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34 (4), 892, 1996.
• 20. WAN Z.M., ZHANG Y.L., ZHANG Q.C., LI Z.L. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83 (1–2), 163, 2002.
• 21. WAN Z. MODIS land surface temperature products users’ guide. University of California, Santa Barbara: Santa Barbara, CA, USA, 2007.
• 22. PINHEIRO A.C.T., MAHONEY R., PRIVETTE J.L., TUCKER C.J. Development of a daily long-term record of NOAA-14 AVHRR land surface temperature over Africa. Remote Sensing of Environment, 103 (2), 153, 2006.
• 23. ATITAR M., SOBRINO J.A. A split-window algorithm for estimating LST from Meteosat 9 data: Test and comparison with in situ data and MODIS LSTs. IEEE Geoscience and Remote Sensing Letters, 6 (1), 122, 2009.
• 24. SNYDER W.C., WAN Z., ZHANG Y., FENG Y.Z. Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19 (14), 2753, 1998.
• 25. JONES P., JEDLOVEC G., SUGGS R., HAINES S. Using MODIS Ts to estimate minimum air temperatures at night. 13th Conference on Satellite Meteorology and Oceanography, Norfolk, VA: AMS 4.13, 2004.
• 26. MOSTOVOY G.V., KING R.L., REDDY K.R., KAKANI V.G., FILIPPOVA M.G. Statistical estimation of daily maximum and minimum air temperatures from MODIS Ts data over the state of Mississippi. GIScience and Remote Sensing, 43 (1), 78, 2006.
• 27. FU G., SHEN Z.X., ZHANG X.Z., SHI P.L., ZHANG Y.J., WU J.S. Estimating air temperature of an alpine meadow on the Northern Tibetan Plateau using MODIS land surface temperature. Acta Ecologica Sinica, 21, 8, 2011.
• 28. Population Census Organization, Statistics Division 2015. Kingdom of Saudi Arabia.
• 29. GRUBBS F.E. Sample Criteria for Testing Outlying Observations. The Annals of Mathematical Statistics, 21 (1), 27, 1950.
• 30. GRUBBS F.E. Procedures for Detecting Outlying Observations in Sample. Technometric, 11 (1), 1, 1969.
• 31. The Land Processes Distributed Active Archive Center (LP DAAC). Available online: https://lpdaac.usgs.gov/ (accessed on 17 October 2016).
• 32. ALMAZROUI M., ISLAM M.N., ATHAR H., JONES P.D., RAHMAN M.A. Recent climate change in the Arabian Peninsula: annual rainfall and temperature analysis of Saudi Arabia for 1978-2009. International Journal of Climatology, 32, 953, 2012.
• 33. http://www.stats.gov.sa/en/sites/default/files/cdsi_data/yb50/Tabels/Chapter2/Table2-3.htm (accessed on 28 November 2016).

Identyfikatory

DOI

10.15244/pjoes/68960